Instructions to use my-ai-stack/Stack-2-9-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use my-ai-stack/Stack-2-9-finetuned with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="my-ai-stack/Stack-2-9-finetuned")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("my-ai-stack/Stack-2-9-finetuned")
model = AutoModelForCausalLM.from_pretrained("my-ai-stack/Stack-2-9-finetuned")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use my-ai-stack/Stack-2-9-finetuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "my-ai-stack/Stack-2-9-finetuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "my-ai-stack/Stack-2-9-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/my-ai-stack/Stack-2-9-finetuned

SGLang

How to use my-ai-stack/Stack-2-9-finetuned with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "my-ai-stack/Stack-2-9-finetuned" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "my-ai-stack/Stack-2-9-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "my-ai-stack/Stack-2-9-finetuned" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "my-ai-stack/Stack-2-9-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use my-ai-stack/Stack-2-9-finetuned with Docker Model Runner:
```
docker model run hf.co/my-ai-stack/Stack-2-9-finetuned
```

walidsobhie-code Claude Opus 4.6 commited on Apr 4

Commit

12c2955

1 Parent(s): 235cb20

fix: correct data path from training-data/final to data/final

Browse files

Files changed (1) hide show

scripts/create_mini_dataset.py +5 -5

scripts/create_mini_dataset.py CHANGED Viewed

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 """
 Create a minimal training dataset for rapid prototyping.
-Samples N examples from the full training-data/final/train.jsonl ensuring tool diversity.
 """
 import argparse
@@ -11,11 +11,11 @@ from pathlib import Path
 from typing import List, Dict
 from collections import defaultdict, Counter
-def load_full_dataset(train_path: str = "training-data/final/train.jsonl") -> List[Dict]:
     """Load the full dataset."""
     path = Path(train_path)
     if not path.exists():
-        raise FileNotFoundError(f"Training data not found at {path}. Please ensure training-data/final/train.jsonl exists.")
     data = []
     with open(path, 'r') as f:
@@ -39,7 +39,7 @@ def extract_tool_calls(example: Dict) -> List[str]:
 def create_mini_dataset(
     output_path: str,
     n_samples: int = 5000,
-    train_source: str = "training-data/final/train.jsonl",
     seed: int = 42
 ):
     """Create a stratified mini dataset."""
@@ -164,7 +164,7 @@ def main():
     parser = argparse.ArgumentParser(description="Create mini dataset for fast prototyping")
     parser.add_argument("--size", type=int, default=5000, help="Number of examples in mini dataset")
     parser.add_argument("--output", type=str, default="./data_mini/train_mini.jsonl", help="Output file path")
-    parser.add_argument("--source", type=str, default="training-data/final/train.jsonl", help="Source full dataset")
     parser.add_argument("--seed", type=int, default=42, help="Random seed for sampling")
     args = parser.parse_args()

 #!/usr/bin/env python3
 """
 Create a minimal training dataset for rapid prototyping.
+Samples N examples from the full data/final/train.jsonl ensuring tool diversity.
 """
 import argparse
 from typing import List, Dict
 from collections import defaultdict, Counter
+def load_full_dataset(train_path: str = "data/final/train.jsonl") -> List[Dict]:
     """Load the full dataset."""
     path = Path(train_path)
     if not path.exists():
+        raise FileNotFoundError(f"Training data not found at {path}. Please ensure data/final/train.jsonl exists.")
     data = []
     with open(path, 'r') as f:
 def create_mini_dataset(
     output_path: str,
     n_samples: int = 5000,
+    train_source: str = "data/final/train.jsonl",
     seed: int = 42
 ):
     """Create a stratified mini dataset."""
     parser = argparse.ArgumentParser(description="Create mini dataset for fast prototyping")
     parser.add_argument("--size", type=int, default=5000, help="Number of examples in mini dataset")
     parser.add_argument("--output", type=str, default="./data_mini/train_mini.jsonl", help="Output file path")
+    parser.add_argument("--source", type=str, default="data/final/train.jsonl", help="Source full dataset")
     parser.add_argument("--seed", type=int, default=42, help="Random seed for sampling")
     args = parser.parse_args()